Field of the Invention
The present invention relates to artificial intelligence systems used to analyze participants engaged in an activity and, in particular, to tracking player role using non-rigid formation priors.
Description of the Related Art
Vision-based systems have been deployed to detect and track players engaged in adversarial team sports. Such vision-based systems may record the position of each player multiple times per second over the course of play. For example, a vision-based system may generate location data for each player at a rate of thirty times per second. These systems may support applications configured to perform various analysis functions, including, characterizing offensive patterns, recognizing individual plays, and predicting the evolution of play in a game or match. Typically, such systems characterize team behavior using a macroscopic representation of the team, such as the position of the centroid of the team players or the distribution pattern of the team players on the playing field, or analyzing a single player over time. Such representations fail to characterize how individual players or teams perform over time, particularly if the role of one or more players changes as play progresses.
In addition, tracking data for players or teams may exhibit high dimensionality, where the quantity of samples collected over long periods of play may be too numerous for applications to efficiently analyze and produce reasonable results. For example, a given set of tracking data may include 200,000 frames of location data from eight different camera angles. Certain temporal analyses may be computationally prohibitive where the tracking data exhibits high dimensionality. Finally, vision systems often do not provide perfect tracking, resulting in false detections or missed detections. Analysis applications relying on tracking data that includes such false detections or missed detections may produce erroneous results. Alternatively, the tracking data may be manually edited to remove any anomalies associated with false or missed detections. However, manually editing tracking data is tedious and time-consuming.